STRATEGIES TO MITIGATE MACHINE LEARNING BIAS AFFECTING OLDER ADULTS: RESULTS FROM A SCOPING REVIEW

Abstract Digital ageism, defined as age-related bias in artificial intelligence (AI) and technological systems, has emerged as a significant concern for its potential impact on society, health, equity, and older people’s well-being. This scoping review aims to identify mitigation strategies used in research studies to address age-related bias in machine learning literature. We conducted a scoping review following Arksey & O’Malley’s methodology, and completed a comprehensive search strategy of five databases (Web of Science, CINAHL, EMBASE, IEEE Xplore, and ACM digital library). Articles were included if there was an AI application, age-related bias, and the use of a mitigation strategy. Efforts to mitigate digital ageism were sparse: our search generated 7595 articles, but only a limited number of them met the inclusion criteria. Upon screening, we identified only nine papers which attempted to mitigate digital ageism. Of these, eight involved computer vision models (facial, age prediction, brain age) while one predicted activity based on accelerometer and vital sign measurements. Three broad categories of approaches to mitigating bias in AI were identified: i) sample modification: creating a smaller, more balanced sample from the existing dataset; ii) data augmentation: modifying images to create more training data from the existing datasets without adding additional images; and iii) application of statistical or algorithmic techniques to reduce bias. Digital ageism is a newly-established topic of research, and can affect machine learning models through multiple pathways. Our results advance research on digital ageism by presenting the challenges and possibilities for mitigating digital ageism in machine learning models.

Older Active Lives Digitally) project, an interdisciplinary collaboration in Scotland and England using intergenerational co-production methods in the creation of digital tools to support older adults' health, well-being and social connectivity.It presents GOALD's multi-year experience of constituting and running intergenerational co-production groups with geographically, socio-demographically and functionally diverse participants to provide feedback and design ideas to business partners for digital devices and applications for health promotion in later life.Mixed methods findings on sources, timing, locations and numbers of participants recruited and retained, their rates of participation, forms, frequency and modes of engagement, as well as organizational partners' and participants' motivations, incentives, capacity and constraints to take part demonstrate the barriers and facilitators of implementing intergenerational co-production, particularly during the Covid-19 pandemic.Our findings highlight lessons learned about the need for creative adaptation of this approach on the ground, and suggest GOALD's potential contributions to new theorisation through enacting and understanding the features of intergenerational co-production and documenting its effects.

STRATEGIES TO MITIGATE MACHINE LEARNING BIAS AFFECTING OLDER ADULTS: RESULTS FROM A SCOPING REVIEW
Charlene Chu 1 , Simon Donato-Woodger 1 , Shehroz Khan 2 , Kathleen Leslie 3 , Tianyu Shi 1 , Rune Nyrup 4 , and Amanda Grenier 1 , 1. University of Toronto,Toronto,Ontario,Canada,2. Toronto Rehabilitation Institute,Toronto,Ontario,Canada,3. Athabasca University,Athabasca,Alberta,Canada,4. University of Cambridge,Cambridge,England,United Kingdom Digital ageism, defined as age-related bias in artificial intelligence (AI) and technological systems, has emerged as a significant concern for its potential impact on society, health, equity, and older people's well-being.This scoping review aims to identify mitigation strategies used in research studies to address age-related bias in machine learning literature.We conducted a scoping review following Arksey & O'Malley's methodology, and completed a comprehensive search strategy of five databases (Web of Science, CINAHL, EMBASE, IEEE Xplore, and ACM digital library).Articles were included if there was an AI application, age-related bias, and the use of a mitigation strategy.Efforts to mitigate digital ageism were sparse: our search generated 7595 articles, but only a limited number of them met the inclusion criteria.Upon screening, we identified only nine papers which attempted to mitigate digital ageism.Of these, eight involved computer vision models (facial, age prediction, brain age) while one predicted activity based on accelerometer and vital sign measurements.Three broad categories of approaches to mitigating bias in AI were identified: i) sample modification: creating a smaller, more balanced sample from the existing dataset; ii) data augmentation: modifying images to create more training data from the existing datasets without adding additional images; and iii) application of statistical or algorithmic techniques to reduce bias.Digital ageism is a newly-established topic of research, and can affect machine learning models through multiple pathways.
Our results advance research on digital ageism by presenting the challenges and possibilities for mitigating digital ageism in machine learning models.

IS MIDDLE-AGED INEQUITY LIKELY TO PERSIST INTO OLD AGE?: TRAJECTORIES OF SOCIAL EXCLUSION IN KOREA Jae Yeon Jung, and Seok In Nam, Yonsei University, Seoul, Republic of Korea
Korea has the highest rate of older adult poverty among the OECD countries.The poverty of the older adult continues even with the government's policies, as the loss of social roles, the reduction of the network, and the difficulty of escaping exclusion are complex.The goal is to find out how the trajectory of social exclusion changes as people get older.We used the data of the Korean Longitudinal Study of Ageing conducted by the Korea Employment Information Service.The target population is middle-aged people aged 45 to 52 (based on the first year), mainly the baby boom generation in Korea, and the total number of cases included in the analysis is 1,348.The trajectory of social exclusion change for 14 years from 2006 to 2020 was confirmed by composing on into economics, health, employment, housing, and social relationships.The group-based multitrajectory model analysis was performed using Stata 16.1.The groups were identified as four: the low exclusion continuous maintenance group, the employment high exclusion maintenance group, the economic/housing high exclusion maintenance group, and the multi-domain high exclusion continuous maintenance group.All four types showed a tendency to maintain social exclusion from middle age until old age.Factors that caused differences between groups were gender, residential area, marital status, education, and income.These findings suggest that as people get older, the cumulative inequity in their lives becomes more pronounced.As a result, it will provide important clues for establishing policies for the welfare of older adults and alleviating social inequality.Older sex workers are marginalised because of their former professional engagement and old age.This study captured the experiences of older female sex workers (FSWs), aged sixty and above, who delivered sexual services at Sonagachi, Asia's largest pleasure market.Exploratory research design was used to understand their lived experiences through a qualitative intervention.The study was content driven, not hypothesis driven.Six older FSWs were selected through purposive sampling, primary data was collected through case study method.Their experiences were recorded starting from childhood, entry into sex trade, daily professional challenges and experiences during old age.Data collected was thematically analysed.Older Sonagachi FSWs are a heterogeneous group in terms of socio-demographic variables, health condition, place and nature of work, average load of clients they entertained per day.This study found that economic adversities and poverty during growing years have been the foremost reasons for choosing sex trade.Many of them remained in the sex industry because of lack of alternative professional opportunities.They experienced various forms of community perpetrated exploitation and some had acquired sexually transmitted infections.Other reasons for them to be in this trade were childhood poverty, early marriage, early motherhood, lack of education, lack of employment, neglect and abuse.They were destitute, had no caregivers, struggled to be part of mainstream society.The study suggests the need to develop policies for vocationally training them to generate alternative income sources and to initiate welfare schemes including pension plans and developing separate old age homes for older FSWs.
Abstract citation ID: igad104.2329POPULATIONAGING IN INDIA: A MICROLEVEL ESTIMATE USING GRIDDED POPULATION DATA The older adult population in Africa is projected to experience the fastest growth rate among regions in the world.Although in most Global North nations, 60-65 is considered old, the issue of what age should be accepted as "old" in Sub-Saharan Africa has remained a controversial issue with governments and scholars.The imminent increase in aging in Africa is a call for action since age definition is directly linked to access to programs and policies necessary to ensure older adults' well-being.Through an online survey, this study investigated the perspectives of Gerontology and Social Work professionals from Sub-Saharan Africa (n=78, 55% female, age range 24-75) on what age should be accepted as "old" or "elderly" for policy development and research purposes.